Method and apparatus for quantitative imaging of blood perfusion in living tissue

ABSTRACT

Embodiments provide methods and systems for imaging, and, more specifically, to a method and apparatus for quantitative imaging of blood perfusion in living tissue. Some embodiments are directed to methods of obtaining quantitative imaging of blood perfusion in living tissues using Doppler optical micro-angiography (DOMAG).

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 13/318,737, filed Nov. 3, 2011 entitled “Method and Apparatusfor Quantitative Imaging of Blood Perfusion in Living Tissue”, which isa 371 of PCT/US2010/033462, which claims priority to U.S. ProvisionalPatent Application No. 61/175,229, filed May 4, 2009, entitled “Methodand Apparatus for Quantitative Imaging of Blood Perfusion in LivingTissue,” the disclosures of which are hereby incorporated by referencein their entirety.

The present application is related to International Publication No.WO2008/039660, filed Sep. 18, 2007, entitled “In Vivo Structural andFlow Imaging,” the disclosure of which is hereby incorporated byreference in its entirety.

GOVERNMENT INTERESTS

This invention was made with Government support under Grant No.R01HL093140 from the National Heart, Lung, and Blood Institute, andGrant No. R01 EB009682 from the National Institute of Biomedical Imagingand Bioengineering. The Government has certain rights in the invention.

TECHNICAL FIELD

Embodiments herein relate to imaging, and, more specifically, to amethod and apparatus for quantitative imaging of blood perfusion inliving tissue.

BACKGROUND

In vivo, three-dimensional mapping of biological tissues and vasculatureis challenging because of the highly-scattering and -absorptive natureof such tissues. Optical coherence tomography (OCT) is a non-invasiveimaging technology that is capable of providing high resolution,depth-resolved cross-sectional images of highly scattering samples. Inaddition, phase-resolved Doppler OCT (PRDOCT), a functional extension ofOCT, may be used to extract velocity information about blood flow infunctional vessels within the scanned tissue beds by evaluating phasedifferences between neighboring A-lines in an OCT B-scan frame. Recentdevelopments in the imaging speed and sensitivity of spectral domainoptical coherence tomography (SDOCT) have allowed PRDOCT to be used forin vivo imaging of blood flow, particularly in human retina. In spectraldomain PRDOCT, the magnitude of Fourier transformation of the spectralinterference fringes is used to reconstruct cross-sectional, structuralimages of the tissue sample, while the phase difference betweenneighboring A-scans is used to extract the velocity information of bloodflow within the scanned tissue. The phase resolved method is based onthe fact that the phase difference of sequential A-lines is linearlyrelated to the flow velocity; thus, the PRDOCT method may be used toobtain quantitative information about the blood flow.

Although the PRDOCT method is of high resolution and high sensitivity tothe blood flow, its imaging performance is greatly deteriorated by atleast two factors: 1) the characteristic texture pattern artifact, whichis caused by optical heterogeneity of the sample, and 2) the phaseinstability that is caused by the sample motion artifacts. Thebackground characteristic texture pattern may be reduced in PRDOCT byusing a dense-sampling approach, e.g., using more A-scans within aB-scan. This dense-sampling approach is effective in reducing thetexture pattern artifacts, but it inevitably leads to a significantincrease of imaging time, which is undesirable for in vivo imagingapplications.

Resonant Doppler imaging may be used to minimize the influence of phaseinstabilities by extracting the flow information from the intensitysignals without extracting the phase. Alternatively, joint spectral andtime domain OCT may be used to rely on analyses of the amplitude andphase distributions of the OCT signals. However, these methods requirerepeated A-scans at the same lateral position, which increases theimaging time.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will be readily understood by the following detaileddescription in conjunction with the accompanying drawings. Embodimentsare illustrated by way of example and not by way of limitation in thefigures of the accompanying drawings.

FIGS. 1A and 1B illustrate a flow diagram (FIG. 1A) and a schematicdiagram (FIG. 1B) of an exemplary optical microangiography (OMAG) systemthat may be used to image the velocities of blood flow, in accordancewith various embodiments;

FIGS. 2A-C illustrate frequency components for different tissue samples:an ideal tissue sample (optically homogeneous sample) with no movingparticles (FIG. 2A), a real tissue sample (optically heterogeneoussample) with no moving particles (FIG. 2B), and a real tissue sample(optically heterogeneous sample) with moving particles (FIG. 2C), inaccordance with various embodiments;

FIG. 3 is a flow chart showing the steps used in an example of Doppleroptical microangiography (DOMAG) for evaluating the velocities of bloodflow from a B-scan dataset, I(k,t), in accordance with variousembodiments;

FIGS. 4A-E illustrate the results of various imaging techniques imagingon a flow phantom: FIG. 4A shoes an OMAG structural image, FIG. 4B showsan OMAG flow image, FIG. 4C shows a DOMAG velocity image, FIG. 4D showsa PRDOCT velocity image, and FIG. 4E shows flow signal profilesextracted from the positions marked in FIG. 4C and FIG. 4D, inaccordance with various embodiments;

FIGS. 5A-C illustrate in vivo OMAG imaging results for an example of atypical B-scan of a mouse brain with the skull left intact: FIG. 5Ashows an OMAG image of microstructures, identical to a conventionalSDOCT image, FIG. 5B shows the corresponding OMAG image of blood flow,and FIG. 5C shows the corresponding DOMAG image of velocities of theblood flow, in accordance with various embodiments;

FIGS. 6A-C illustrate in vivo 3D OMAG imaging of the cortical brain of amouse with the skull left intact; the volumetric visualization wasrendered by merging the 3D micro-structural image with the 3D cerebralblood flow image (FIG. 6A), the 3D signals of cerebral blood flow only(FIG. 6B), and the corresponding DOMAG imaging of velocities (FIG. 6C)within the 3D blood flow network in FIG. 6B, in accordance with variousembodiments;

FIGS. 7A and 7B illustrate a maximum projection view (x-y) of OMAG (FIG.7A) and DOMAG (FIG. 7B) of the cerebral blood flow in the cortical brainof the mouse shown in FIG. 6, in accordance with various embodiments;

FIGS. 8A-D illustrate a comparison between OMAG and PRDOCT B-scanimaging of the cortical brain in mice in vivo: OMAG structural image(e.g., SDOCT; FIG. 8A) and the corresponding OMAG flow image (FIG. 8B),DOMAG flow velocity image (FIG. 8C), and PRDOCT flow velocity image(FIG. 8D), in accordance with various embodiments;

FIGS. 9A and B illustrate a comparison between 3D OMAG and PRDOCTimaging of the cortical brain in mice with the intact skull in vivo,showing the maximum x-y projection views of an OMAG cerebral blood flowimage (FIG. 9A), a DOMAG flow velocity image (FIG. 9B), and a PRDOCTflow velocity image (FIG. 9C), in accordance with various embodiments;

FIGS. 10A-C illustrate a 3D plot of an example of a typical B-scan offlow images: FIG. 10A shows a conventional PRDOCT flow image withoutsegmentation, FIG. 10B shows a conventional PRDOCT flow image withsegmentation; and FIG. 10C shows a Doppler OMAG flow image, inaccordance with various embodiments;

FIG. 11 illustrates a flow chart of an example of a phase-only filteringprocess, in accordance with various embodiments; and

FIGS. 12A-C illustrate in vivo phase-only filtering imaging results foran example of a typical B-scan of a mouse brain: FIG. 12A shows a FDOCTstructure image; FIG. 12B shows a phase-only filtering flow image; andFIG. 12C shows a phase-only filter DOMAG image of velocities of bloodflow, in accordance with various embodiments.

DETAILED DESCRIPTION OF DISCLOSED EMBODIMENTS

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof, and in which are shownby way of illustration embodiments that may be practiced. It is to beunderstood that other embodiments may be utilized and structural orlogical changes may be made without departing from the scope. Therefore,the following detailed description is not to be taken in a limitingsense, and the scope of embodiments is defined by the appended claimsand their equivalents.

Various operations may be described as multiple discrete operations inturn, in a manner that may be helpful in understanding embodiments;however, the order of description should not be construed to imply thatthese operations are order dependent.

The description may use perspective-based descriptions such as up/down,back/front, and top/bottom. Such descriptions are merely used tofacilitate the discussion and are not intended to restrict theapplication of disclosed embodiments.

The terms “coupled” and “connected,” along with their derivatives, maybe used. It should be understood that these terms are not intended assynonyms for each other. Rather, in particular embodiments, “connected”may be used to indicate that two or more elements are in direct physicalor electrical contact with each other. “Coupled” may mean that two ormore elements are in direct physical or electrical contact. However,“coupled” may also mean that two or more elements are not in directcontact with each other, but yet still cooperate or interact with eachother.

For the purposes of the description, a phrase in the form “NB” or in theform “A and/or B” means (A), (B), or (A and B). For the purposes of thedescription, a phrase in the form “at least one of A, B, and C” means(A), (B), (C), (A and B), (A and C), (B and C), or (A, B and C). For thepurposes of the description, a phrase in the form “(A)B” means (B) or(AB) that is, A is an optional element.

The description may use the phrases “in an embodiment,” or “inembodiments,” which may each refer to one or more of the same ordifferent embodiments. Furthermore, the terms “comprising,” “including,”“having,” and the like, as used with respect to embodiments, aresynonymous.

In various embodiments, methods, apparatuses, and systems forquantitative imaging of blood perfusion in living tissue are provided.In exemplary embodiments, a computing device may be endowed with one ormore components of the disclosed apparatuses and/or systems and may beemployed to perform one or more methods as disclosed herein.

Embodiments herein provide methods and apparatus for the quantitativeassessment of blood flow and perfusion in living tissue at the capillarylevel of resolution. In embodiments, parameters that may be measuredinclude the blood flow rate in individual blood vessels, the blood flowperfusion rate within a scanned tissue volume, measurement of bloodvessel diameters (inner diameter), blood vessel density within a scannedtissue volume, and vessel tortuosity. Embodiments also may providedigital reconstruction of the tissue background to address backgroundnoise and enhance imaging results.

The disclosed embodiments may be used in a variety of applications, suchas the optical coherence tomography imaging of human retina. Otherapplications include a wide range of clinical and research areas havingvascular involvement, such as cancer, neurovascular diseases, diabetes,eye diseases, ear diseases, cardiovascular diseases, skin diseases, aswell as in small animal studies, and the like.

Various embodiments provide a novel imaging technique called Doppleroptical microangiography (DOMAG) that permits quantitative imaging ofblood perfusion in living tissue. Unlike the phase-resolved Doppler OCT(PRDOCT) method, optical microangiography (OMAG) utilizes implicitly thephase information embedded in the OCT spectral interferograms. Inembodiments, OMAG uses heterodyne technology to separate (a) thescattering signals caused by the moving scatters, from (b) thescattering signals caused by the static tissue background (e.g., thetissue microstructures). In addition to its ability to achievemicro-structural imaging, OMAG provides volumetric vasculature imagingwithin a scanned tissue bed at capillary-level resolution. Inembodiments, OMAG may be used to image, for instance, cerebral bloodperfusion and blood flow within human retina and choroid. In someembodiments, an advantage of OMAG is that only the signals backscatteredby the functional blood appear in the OMAG flow output plane, makingblood flow imaging substantially free of artifact-induced noise.

For the purposes of describing embodiments of the disclosure, the term“A-line” or “A-scan” refers to an axial scan (a line along the depth orz axis direction). The term “B-line” or “B-scan” refers to a collectionof a number of A-scans captured when a probe beam scans over a sample inthe lateral or x direction. “C-scan” refers to a collection of a numberof B-scans captured when the probe beam scans over a sample in anelevational or y direction.

As described herein, DOMAG may be used in some embodiments to evaluatethe velocities of OMAG flow signals by measuring the phase differencebetween neighboring A-lines. In embodiments, the method may use a PRDOCTapproach to evaluate the phase difference between neighboring OMAGA-lines, however, the application of the phase-resolved technique mayrequire a correlation between neighboring A-scans. This correlationrequirement may make extraction of the blood flow velocities in OMAGdifficult because in the OMAG flow image, the regions that are occupiedby the microstructural signals are rejected by OMAG, which may lead to aloss of the correlation between neighboring A-scans in these regions.

In embodiments, to overcome this problem, an ideal static backgroundtissue may be digitally reconstructed that is totally opticallyhomogeneous to replace the real heterogeneous tissue sample in OMAG.This ideal background tissue may provide a constant background signalthat makes the neighboring A-scans totally correlated, which may lead toa dramatic increase in the phase signal to noise ratio (SNR) for thephase-resolved signals that represent flow velocities.

FIG. 1A illustrates an exemplary embodiment of an opticalmicroangiography (OMAG) system 100 that may be used to image thevelocities of blood flow.

The illustrated OMAG system 100 may include some features known in theart, features which may not be explained in great length herein exceptwhere helpful in understanding embodiments of the present disclosure.

As illustrated, the OMAG system 100 includes a light source 10. Thelight source 10 may be any light source suitable for the purpose,including, but not limited to, a broadband light source or a tunablelaser source. A suitable broadband light source may include asuperluminescent diode. In one embodiment, the light source 10 maycomprise a superluminescent diode (for instance, from DenseLight,Singapore) with a central wavelength of 1310 nanometers (nm) and aspectral bandwidth of 56 nm, which may provide an axial imagingresolution of about 13 micrometers (μm) in air. In various embodiments,the light source 10 may be a light source having shorter or longerwavelengths, or may provide more than one wavelength. In various otherembodiments, light source 10 may comprise a tunable laser source suchas, for example, a swept laser source.

The OMAG system 100 includes a fiber coupler 12 for splitting the lightfrom light source 10 into two beams: a first beam provided to areference arm 14 and a second beam provided to a sample arm 16. Invarious embodiments, fiber coupler 12 may comprise a 2×2 fiber coupleror any fiber coupler suitable for the purpose. In particularembodiments, the light from light source 10 may be coupled into afiber-based interferometer (for example a Michelson interferometer) viaan optical circulator 11 (see, e.g., FIG. 1B) prior to being routed tofiber coupler 12. Further embodiments also may include a laser diode 13(see, e.g., FIG. 1B), for instance a 633 nm laser diode, that may coupleto fiber coupler 24.

Sample arm 16 may be configured to provide light from light source 10 toa sample 28 by way of a polarization controller 24 and a probe 26. Probe26 includes a scanning device (for example a pair of x-y galvanometerscanners 27, 29) (see, e.g., FIG. 1B) for scanning the probe beam oversample 28 in the x and y directions. Probe 26 also comprises theappropriate optics, such as a collimating lens 31 and/or an objectivelens 33 (see, e.g., FIG. 1B) for delivering the light onto sample 28. Invarious embodiments, probe 26 also receives backscattered light fromsample 28. Although the characteristics of the light provided to sample28 may depend on the particular application, in some embodiments, forinstance with a 50 mm focal length of the objective lens, the lateralimaging resolution may be approximately 16 μm, which may be determinedby an objective lens that focuses light onto sample 28, with a lightpower on sample 28 being approximately 1 milliwatt (mW).

Reference arm 14 may be configured to provide a reference light to adetection arm 30 (discussed more fully below), from the light providedby light source 10, for producing a spectral interferogram incombination with backscattered light from sample 28. Reference arm 14includes optics 20 and a mirror 22 for reflecting light from lightsource 10 in order to provide the reference light. Optics 20 mayinclude, but are not limited to, various lenses suitable for thepurpose, for instance a collimating lens and/or an objective lens (notshown). In embodiments, the zero delay line of the system may be set atabout 0.5 mm above the focus spot of sample 28.

Mirror 22 may be stationary or may be modulated by a modulator 23.Modulation may be equivalent to frequency modulation of the detectedsignal at the detection arm 30. In embodiments, spectral interferencesignals (interferograms) may be modulated by a constant Dopplerfrequency, ω₀, by a modulated mirror 22 in reference arm 14, themodulation making it feasible to separate the moving and staticcomponents within sample 28. The spectral interference signal may thenbe recovered by de-modulating the modulated signal at the modulationfrequency, ω₀. De-modulation may be achieved using any suitable methodincluding, for example, a digital or optical de-modulation method. Inembodiments, modulation and de-modulation of spectral interferencesignals may advantageously improve the signal-to-noise ratio, resultingin an improved image quality for structural, flow, and angiographicimaging.

Various methods may be used for modulating mirror 22. For example, invarious embodiments, modulator 23 may be a linear piezo-translationstage onto which mirror 22 is mounted. The piezo-translation stage maybe configured to move mirror 22 at some constant velocity across aB-scan (e.g., the x direction scan). In an exemplary embodiment, mirror22 is mounted onto a piezo-translation stage driven by a 10 Hz saw-toothwaveform with an amplitude of 50 μm. In various other embodiments,however, modulator 23 may be a phase-modulating device (e.g., anelectro-optic phase modulator or acoustic phase modulator) or anothersuitable device for introducing a suitable Doppler frequency modulation.In various embodiments, the optical path-length in the reference arm orin the sample arm may be modulated which has the same or similar effectas moving mirror 22 back and forth at a constant speed. In anembodiment, a method of stretching the optical fiber may be used. Invarious embodiments, the modulation of the interferogram may also beprovided by probe 26. In an exemplary embodiment, probe 26 may beconfigured such that the input signal is scanned with an offsetreference to the pivot point.

In various embodiments, modulation may be simply provided by the movingparticles, for example the flowing blood cells in the patent vesselwithin tissue sample. In this case, the reference mirror 22 may bestationary.

The light returning from reference arm 14 and the light returning fromsample arm 16 (e.g., the spectral signal) may be recombined and coupledinto a single mode fiber by a coupler 12 for introduction to a detectionarm 30. As illustrated, detection arm 30 includes a spectrometer 34 andone or more of various optics 36 including, but not limited to, one ormore collimators, one or more diffracting/transmission gratings, and oneor more lenses (not illustrated). In exemplary embodiments, optics 36may include a 30-millimeter (mm) focal length collimator, a 1200lines/mm diffracting grating, and an achromatic focusing lens with a 100mm focal length. Such parameters are exemplary and may be modified in avariety of ways in accordance with the embodiments disclosed herein.

In embodiments employing a broadband light source, spectrometer 34 mayinclude a detector array such as a charge-coupled device (CCD) 38configured to detect a spectral interference signal. CCD 38 may includeone or more of a line-scan camera and an area scan camera. An exemplarysuitable CCD 38 may be a CCD consisting of a 14-bit, 1024 pixel InGaAsline scan camera. In one specific example, the maximum line scan rate ofthe camera may be about 47 KHz, and the spectrometer setup may have adesigned spectral resolution of about 0.141 nm, which may give ameasured imaging depth of about 3.0 mm on each side of the zero delayline. For those embodiments wherein light source 10 comprises a tunablelaser rather than a broadband light source, however, OMAG system 100 mayinclude a diffusion amplifier that may comprise one or more singleelement detectors rather than a spectrometer 34. For example, one ormore dual-balanced photo-diode detectors may be used.

As illustrated, reference arm 14, sample arm 16, and detection arm 30include polarization controllers 18, 24, and 32, respectively.Polarization controllers 18, 24, 32 may be configured to fine-tune thepolarization states of light in the OMAG system 100. Although an OMAGsystem within the scope of the present disclosure may include more orfewer polarization controllers than illustrated, the provision ofpolarization controllers 18, 24, and 32 in reference arm 14, sample arm16, and detection arm 30, respectively, may advantageously maximize thespectral interference fringe contrast at the CCD 38 (or other suitabledetector).

In various embodiments, OMAG system 100 includes one or more userinterfaces 40 for one or more purposes including displaying images,input of data, output of data, etc.

OMAG system 100 may be configured to build a 3-D data volume set byscanning sample 28 with a sample light in x, y, and λ (z) directions toobtain a 3-D spectral interferogram data set. In exemplary embodiments,probe 26 may be scanned in the lateral direction (x direction) by anx-scanner and in the elevational direction (y direction) by a y-scanner.In various embodiments, the x-scanner may be driven by a 10 Hz saw-toothwaveform with an amplitude equivalent to 2.2 mm, and the y-scanner maybe driven at 0.02 Hz with an amplitude of 2.2 mm. In embodiments, thisconfiguration may determine a line scan rate of about 31 KHz for thecamera. In this same specific, non-limiting example, the imaging ratemay be set at 20 frames (B-scan) per second (fps), and each B-scan mayhave a 2.5 mm span over the sample, including 1500 A-lines. Inembodiments, this may represent an over sampling factor of about 10because the lateral resolution of the system may be about 16 μm. In thisexample, in the elevational direction, there may be 500 discrete pointsalong about 2.5 mm, e.g., 500 B-scans. Hence, the data cube of each 3Dimage (C-scan) in this exemplary embodiment may be composed of 1024 by1500 by 500 (z-x-y) voxels, which may take about 25 seconds to acquire.In specific examples, the operations for probe beam scanning, dataacquisition, data storage and hand-shaking between them may becontrolled by a custom software package written, for example, in C++language. In this example, 500 B-scans for a C-scan represented oversampling in the elevational direction. In practice, however, 200 B-scansmay be sufficient to obtain the volumetric images, leading to a temporalresolution of about 10 seconds for 3D OMAG imaging.

As discussed above, the DOMAG method may use the OMAG method to obtainoptical signals, for instance optical signals backscattered by bloodcells in blood vessels, through rejecting the heterogeneous tissuesignals, e.g., the optical signals backscattered by microstructures ofthe tissue sample. In embodiments, the method may then use a phaseresolved DOCT technique to extract the flow velocity information. Atfirst glance, applying the phase-resolved technique to extract flowvelocities from OMAG signals of blood flow may appear straightforward.However, the technique assumes a correlation between neighboring OCTA-scan signals. In various embodiments, such a correlation is notachievable in the background tissue regions in OMAG, making theextraction of flow velocities difficult. In embodiments, the DOMAGmethod described herein may circumvent this difficulty.

In various embodiments, the spectral interference signal captured byeach pixel of the CCD camera in OMAG/SDOCT is essentially the sameexcept for the wavelength, λ. In embodiments, the method assumes thatthe wavenumbers of the broad band light source is from k₀ to k₀+Δk,where k₀=2π/λ₀, and these wavenumbers cover 1024 pixels of the line scancamera. As a consequence, the camera may record the spectralinterference fringe signal formed between the reference light and thelight backscattered from within sample, which may be written as afunction of kj:

I(k _(j))=S(k _(j))

|E _(R)exp(i2k _(j) r)+∫_(−∞) ^(∞) a(z)exp{i2k _(j) [r+nz]}dz| ²

j=1,2,3 . . . 1024  (1)

where i=√−1, < > is the time average, k_(j) is the wavenumber of thelight captured by the jth detector (pixel) of the CCD camera, I(k_(j))is the light intensity captured by the jth detector, S(k_(j)) is thespectral density of the light source at k_(j), r is the optical pathlength for the light traveled in the reference arm, n is the refractiveindex of the sample, a(z) is the magnitude of the light backscattered atdepth z. E_(R) represents the magnitude of the reference light. In thisembodiment, each B-scan may contain 1500 A-lines and covers about 2.5 mmin the lateral direction. So the signal captured by the jth pixel ineach B-scan may be written as a function of the time variable t thatrelates to the position of the focus beam spot on the sample, steered bythe X scanning mirror.

I(k _(j) ,t)=S(k _(j))

|E _(R)exp(i2k _(j) r)+∫_(−∞) ^(∞) a(z,t)exp{i2k _(j) [r+nz]}dz| ²

  (2)

In embodiments, because the light backscattered from the sample is quiteweak compared to the light reflected from the reference mirror, the selfcross-correlation between the light backscattered from differentpositions within the sample is not considered. The DC signals are alsonot considered in various embodiments because they do not contribute touseful OMAG signals. In these cases, Eq. (2) may be written as:

I(k _(j) ,t)=2S(k _(j))E _(R)∫_(−∞) ^(∞) a(z,t)cos(2k _(j) nz)dz  (3)

It is clear that Eq. (3) is constant if the sample is totally opticallyhomogeneous, which means that a(z,t) and n do not vary within the entiresample. In embodiments, if this is the case, then the spatial frequencycomponents of the sample in the lateral direction presented by Eq. (3)will be a delta function, which is shown as an arrow in FIG. 2A.However, in real situations, the imaging sample may be opticallyheterogeneous, which means that a(z,t) and n are functions of the timevariable t. Thus, Eq. (3) may be expressed as:

I(k _(j) ,t)=2S(k _(j))E _(R)∫_(−∞) ^(∞) a(z,t)cos(2k _(j)n(z,t)z)dz  (4)

As a consequence, in various embodiments, Eq. (4) may not be constantanymore. In embodiments, the intensity captured by the CCD camera may bemodulated by the heterogeneous properties of the sample along eachB-scan. The spatial frequency components of a static tissue sample,which are referred to herein as the heterogeneous frequencies, may beexhibited as a randomly distributed function around zero frequency witha bandwidth (BW) as shown in the curve in FIG. 2B.

In various embodiments, such as when there is a patent blood vesselburied within a motionless tissue at position (z₁,t₁), it may be assumedthat the blood cells (e.g., the scattering particles) within the vesselmay move toward the incident beam at a velocity v. The frequency of thelight backscattered from these blood cells may be modulated by itsvelocity. Then, Eq. (4) may be expressed as:

I(k,t)=2S(k _(j))E _(R)[∫_(−∞) ^(∞) a(z,t)cos(2k _(j) n(z,t)z)dz+a(z ₁,t ₁)cos [2k _(j) n(z ₁ ,t ₁)(z ₁ −vt)]]  (5)

In this embodiment, the self cross-correlation signal from within thesample is also not considered. The 1^(st) term on the right side of Eq.(5) represents backscattering signals from a static sample withreflectivity of a(z,t), while the 2^(nd) term represents backscatteringfrom the moving particles with reflectivity of a(z₁,t₁) with a velocityof v at position (z₁,t₁). In various embodiments, moving particles mayproduce a frequency shift caused by the Doppler effect of the movingparticles. This is illustrated in FIG. 2C, where the second curve is theDoppler beating frequency part. As discussed, the phase-resolvedtechnique may require a correlation between neighboring A-scans todetermine v of the moving particles, and the correlation, inconventional PRDOCT, provided by the 1^(st) term on the right-side ofEq. (5) also may be required to suppress the noise signals in thenon-flow regions in order to increase the flow imaging contrast. Becauseof the optical heterogeneity n(z,t) of a tissue, n(z,t) may impose anoise background onto the blood flow signals, making it difficult forPRDOCT to measure precisely the blood flow velocity, particularly incapillaries. In contrast, in embodiments, OMAG may eliminate the 1^(st)term on the right side of Eq. (5) in order to image blood flow. Thiselimination may minimize or reduce the noise production due to n(z,t),but may result in OMAG losing its correlation condition betweenneighboring A-scans for the heterogeneous tissue regions. Consequently,the phase-resolved technique may not be directly applied to OMAG bloodflow signals. Thus, various embodiments may employ the strategy ofdigitally reconstructing an ideal sample background with a constantbackscattering coefficient a₀ and a refractive index n₀ throughout thesample, as illustrated in Eq. (6)—thus creating a totally homogeneoussample which may reinforce a complete correlation among OMAG A-scansignals.

I ₀(k _(j) ,t)=2S(k _(j))E _(R)∫_(−∞) ^(∞) a ₀(z,t)cos(2k _(j) n₀(z,t)z)dz  (6)

where a₀(z,t)≡a₀ and n₀(z,t)≡n₀ throughout the scanned tissue sample. Inembodiments herein, a₀=10⁻⁶ and n₀=1.35 when constructing the homogenoustissue background using Eq. (6). These values are taken according to thetypical optical properties of biological tissues, e.g., the averagereflectivity may be between 10⁻⁴ and 10⁻⁷ and the average refractiveindex may be 1.35.

In various embodiments, the digitally reconstructed homogenous tissuesample may subsequently replace the first term on the right side of Eq.(5). In doing so, OMAG blood flow signal now becomes:

I′(k _(j) ,t)=2S(k _(j)){E _(R) ∫a ₀ cos(2k _(j) n ₀ z)dz+E _(R) a(z_(j) ,t ₁)cos [2k _(j) n(z ₁ ,t ₁)(z ₁ −vt)]}  (7)

In various embodiments, the construction of the ideal tissue sample maynot affect OMAG signals of blood flow because it may only replace thetissue background signals with a homogeneous background withoutaffecting the blood flow signals within the B-scan. If the time variablet is treated as a constant and a Fourier transformation is applied uponwavelength k, then:

{tilde over (I)}(z,t)=FT ⁻¹ {I(k _(j) ,t)}|_(k) =A(z,t)exp[iφ(z,t)]  (8)

where ψ(z, t) is the phase of the analytic signal. The phase differencebetween neighboring A-scans, n and n−1, is then evaluated:

$\begin{matrix}{{\Delta \; {\phi \left( {z,t} \right)}} = {{\tan^{- 1}\left\lbrack \frac{{Im}\left\lbrack {{\overset{\sim}{I}\left( {z,t_{n}} \right)} \cdot {{\overset{\sim}{I}}^{*}\left( {z,t_{n - 1}} \right)}} \right\rbrack}{{Re}\left\lbrack {{\overset{\sim}{I}\left( {z,t_{n}} \right)} \cdot {{\overset{\sim}{I}}^{*}\left( {z,t_{n - 1}} \right)}} \right\rbrack} \right\rbrack}.}} & (9)\end{matrix}$

Based on the linear relationship between phase difference betweenneighboring A-lines and velocity, the velocity of flow signal imaged byOMAG may be directly written as:

$\begin{matrix}{{v\left( {z,t} \right)} = \frac{\lambda \; \Delta \; {\phi \left( {z,t} \right)}}{4\pi \; n\; \Delta \; t}} & (10)\end{matrix}$

where v(z,t) is the flow velocity at depth z, Δt is the time intervalbetween neighboring A-lines, and n is the refractive index of thesample. In embodiments, there may be a small constant offset induced bya₀ in Eq. (8), which may perturb the evaluated Δφ(z, t). However, thesmall offset is usually at least two orders of magnitude smaller thanthe OMAG flow signals, leading to a negligible effect on the finalevaluated Δφ(z, t).

As disclosed herein, an InGaAs camera may be used in various embodimentsto capture the interferograms at 31 KHz A-scan rate. Thus, the maximumdetectable flow velocity that does not undergo phase-wrapping may be 10mm/s for the OMAG system used (FIG. 1). In embodiments, the modulationfrequency fc=400 Hz may be selected for OMAG to filter out theheterogeneous frequencies that represent the static tissue components.This value may be empirically determined from the tissue samples used,which may correspond to a minimal flow velocity of ˜0.26 mm/s that maybe detected by the system. FIG. 3 provides a flow chart illustrating howDOMAG works in some embodiments to obtain final velocity images of bloodflow of the scanned tissue sample. The data coordinates are indicated inthe lower right corner of each data block, where t is the time variableof probe beam scanning over a sample, k is the wavenumber, f is thespatial frequency, and z is the imaging depth. FT|_(t) represents theFourier transform (FT) against the time variable t in the B-scan,FT⁻|_(f) indicates the inverse FT against the spatial frequency, f, andFT|k is FT against the wavenumber k.

In various embodiments, to validate the efficacy of the disclosedmethod, the method was performed on a flow phantom. In one specific,non-limiting example, the phantom was made from gelatin mixed with 2%milk to simulate the background optical heterogeneity of the tissue inwhich a capillary tube with an inner diameter of about 200 μm wassubmerged and a ˜2% TiO₂ particle solution was flowing in it. Theinclining angle of the tube toward the incident beam, e.g., the Dopplerangle, was set at about 85°. The flow rate of the particle solution wascontrolled by a precision syringe pump to a range that falls within thedetectable range of the OMAG system, e.g., velocity between 0.2 and 10mm/s. In this embodiment, each B-scan (lateral direction) contained 1000A-lines covering 2.5 mm. Thus, the corresponding Δx between neighboringA-lines was 2.5 μm. The results are shown in FIG. 4.

FIG. 4A shows the OMAG structural image of the scanned tissue phantomthat is identical to the image obtained by conventional SDOCT, while thecorresponding OMAG flow image is shown in FIG. 4B. It can be seen thatOMAG successfully delineates the fluid flow within the capillary tubewith the background signals from the non-flow region of phantom beingrejected. However, OMAG does not provide the velocity information offlowing particles within the capillary tube. The fluid flow velocityinformation was then evaluated by DOMAG as described in Section 3 (FIG.4C), and PRDOCT (FIG. 4D) methods, respectively.

In various embodiments, it is clear that DOMAG provides superior imagingperformance because the background phase noise is maximally suppressedin FIG. 4C as compared to FIG. 4D. In embodiments, the phase noisesuppression may occur in the entire output plane due to the digitalreconstruction of the ideal tissue phantom in DOMAG. This may beadvantageous because it is not necessary to use a segmentation method tosegment the tissue regions of interest so as to exclude low signalregions when evaluating useful flow velocity signals, as is normallydone in conventional PRDOCT. In embodiments, this may lead to a reduceddemand for computing power.

To better show the noise suppression by DOMAG, FIG. 4E illustrates thesignal profiles across the B-scan at the depth positions marked in FIGS.4C and 4D, respectively. The curve was extracted from the locationsmarked with the line in FIG. 4C, while the curve indicates these samelocations in FIG. 4D. In this embodiment, the phase differences causedby the flowing particles (e.g., the parabolic curve) were almost thesame using these two methods, however the noise background in DOMAG ismuch smaller than that in PRDOCT.

In another embodiment, to quantitatively evaluate the improvementprovided by DOMAG, the flow velocity signals were calculated, as well asthe phase noise levels. Two regions were defined from the structuralimage (FIG. 4A): flow signal region Ω^(S), marked as the circle, andnoise region Ω^(N), enclosed by the lines. The flow signal region wasdetermined by segmenting the lumen of the capillary tube, while thenoise region was determined by segmenting the micro-structural signalsfrom the structural image of the scanned phantom. The segmentations werestraightforward because in this simple embodiment, it was known wherethe flow was located. Two masks were produced from the resulting tworegions, and were used in combination with the DOMAG image (FIG. 4C) andPRDOCT image (FIG. 4D) to calculate the phase signals in the respectiveregions. In some embodiments, it is not necessary to perform thesegmentation to evaluate the phase noise in DOMAG, however, to make it afair comparison between DOMAG and PRDOCT, the phase differences withinexactly the same regions were evaluated for both the methods.

The phase noise level in the velocity image was calculated by evaluatingthe standard deviation of the phase differences, Δφ, between neighboringA-lines within region Ω^(N),

$\begin{matrix}{\sigma_{\Delta \; \phi} = \sqrt{\frac{1}{M - 1}{\sum_{\Omega^{N}}\left( {{\Delta \; \phi} - \overset{\_}{\Delta \; \phi}} \right)^{2}}}} & (11)\end{matrix}$

where M is the total number of pixels within the region Ω^(N). Δφ is theaverage value of the phase differences Δφ. The useful flow signals weredefined by following algorithm in the flow region Ω^(S):

S=Σ _(Ω) ^(S)(Δφ>σ_(Δφ))×Δφ  (12)

where the bracket term represents a binary operation for which itreturns 1 if Δφ is larger than σ_(Δφ), or else it returns to 0. In doingso, the value S represents the effective detectable signals that aretreated as useful signals of flow velocities for the target flow.Finally, the phase SNR may be defined by:

Phase SNR=20×log(S/σ _(Δφ))  (13)

In various embodiments, the phase SNR indicates a metric for imagingcontrast, rather than the phase sensitivity of the system used. Inembodiments, the phase sensitivity for both DOMAG and PRDOCT maygenerally be the same, which is determined by the system setup and thebeam scanning pattern over the sample.

The results are tabulated in Table 1 for both the OMAG and PRDOCTmethods. In this example, compared to PRDOCT, the phase noise σ_(Δψ) wasreduced from 0.43 rad to 0.037 rad for DOMAG, which represents a morethan 11-fold improvement. The detectable effective velocity signals werealso improved, from 4198 to 4395. Consequently, the phase SNR wasincreased by 22 dB, from 79 dB to 101 dB.

TABLE 1 Evaluated phase noise, effective signal and phase SNR of phasedifferences for DOMAG and PRDOCT σ_(Δφ) S Phase SNR DOMAG 0.037 rad 4395rad 101 dB  PRDOCT  0.43 rad 4189 rad 79 dB Improvement 11.6 (times) 4%(unitless) 22 dB

From the above analyses, it is apparent that the noise level in DOMAGmay be greatly reduced. In PRDOCT, the phase noise level is often termedas the phase sensitivity, which can be determined by the intensitysignal to noise ratio of the OCT system, X, by the following equation:

$\begin{matrix}{\sigma_{\Delta \; \phi}^{2} = \left( \frac{1}{X} \right)} & (14)\end{matrix}$

In various embodiments, the phase sensitivity value calculated from Eq.(14) represents the upper limit that the phase resolved method canachieve under the total correlation condition between neighboringA-scans, which might be met in PRDOCT by repeated A-scans at the samesample position. For the examples described herein, the OCT intensitySNR for the flow region was about 30 dB, which is quite common in thecase of imaging in vivo. Thus, the corresponding phase sensitivity wasabout 0.0316 rad. Thus, the phase sensitivity of DOMAG evaluated throughthe B-scan was quite close to that of PRDOCT evaluated from the repeatedA-scans at the same sample position, demonstrating the power of thedisclosed DOMAG method for in vivo imaging of blood flow within themicrocirculatory tissue beds.

In various embodiments, in vivo tests were performed to validate theefficacy of DOMAG for non-invasive assessment of microcirculation withintissue beds. In embodiments, the capability of DOMAG to image thecerebral blood perfusion in mouse models was demonstrated with the skullleft intact. The mouse brain was selected for use in part because thebrain is one of the least accessible organs for non-invasiveobservations of blood perfusion (as in the human brain). The 3-month oldadult mouse, weighing about 25 g, was shaved to remove hair from thehead before optical imaging. The mouse was then anesthetized by using 2%isoflurane (0.2 L/min O2, 0.8 L/min air), and positioned in astereotaxic stage to minimize movement. The body temperature was kept atabout 37° C. Before the OMAG data acquisition, a window on the head wascarefully made by removing the overlaying skin to allow OMAG imaging ofthe cerebral blood flow within the cortex through the intact skull. Theexposed skull was washed by saline to prevent it from dehydrating. Thewhole imaging session lasted about 30 minutes, including about 25seconds for optical imaging data acquisition.

Shown in FIG. 5 are the representative results from a single B-scan(frame) of a mouse brain. FIG. 5A is the OMAG structural image,identical to the cross-sectional image obtained from conventional SDOCT,from which the important histological layers were clearly delineated,including the cranium (skull), gray matter (cortex) and white matter.FIG. 5B shows the corresponding OMAG image of localized blood flow thatpermeates this cross-section (FIG. 5A). However, this image onlyprovides the backscattered signals from functional blood that does notindicate the flow velocity information, which is needed for quantifyingblood perfusion. Applying the DOMAG method, the velocity information asto the imaged blood flow may be extracted from FIG. 5B. The result isgiven in FIG. 5C, which represents an image of the DOMAG phasedifferences, Δφ(x, z) that may be converted to the velocity values byEq. (10). The blood flow velocity in capillaries (indicated by whitearrows, for example) is imaged by DOMAG.

In various embodiments, the 3D imaging in the OMAG system was achievedby scanning the focused sample beam over the skull using the X-Y scanner(FIG. 1). The field of view for the system was 2.5 mm by 2.5 mm (x-y),which contained 1500 A-scans in the x-direction (B-scan) and 500 B scansin the y-direction (C-scan). The original raw data cube (spectralinterferograms) was first processed frame by frame, and then theresulting images, including structural, flow and velocity images, wererecombined to produce 3D volumetric visualization of the scanned tissuevolume.

The results for an example of a typical tissue volume of 2.5×2.5×2.0 mm³are given in FIG. 6. FIG. 6A is a volumetric visualization rendered bymerging the micro-structural 3D image (via SDOCT) with the corresponding3D image of functional blood flows (via OMAG), where the preciselocations of blood flow may be identified within microstructures of thesample. In the image, a cutaway view is used to appreciate how the bloodvessels innervate the tissue volume. FIG. 6B shows the volumetricnetwork of patent blood vessels within the scanned tissue volume, wherethe smallest diameter of blood vessels was identified at about 15 μm,close to the system spatial resolution (about 16 μm). The correspondingvelocity information for the imaged blood flows is shown in FIG. 6C,evaluated by use of DOMAG. In FIG. 6C, the directional flow informationis indicated. The physical image size was 2.5×2.5×2.0 (x-y-z) mm³.

To show in detail the blood vessel networks and blood flow velocitieswithin them, the maximum projection approach was used to obtain x-yprojection images. Together with the blood vessel perfusion networks(FIG. 7A), Doppler OMAG (FIG. 7B) provides a powerful tool to quantifyblood perfusion within the microcirculation tissue beds in vivo.

Because both the DOMAG and PRDOCT methods are capable of providing thevelocity information for the blood flows within the living biologicaltissue, a comparison between these two techniques for in vivo imaging isprovided. In embodiments, the final imaging results may be differentunder different system setups, for example for the imaging speed and thenumber of A-scans used in a single B-scan. Thus, for a fair comparison,the same data set was used for each, which was obtained from a mousebrain with the skull left intact under the same system configurations.For this set of embodiments, the imaging speed was 20 KHz A-scan rate.FIG. 8 shows the results from an example of a typical B-scan obtainedfrom the cortical brain of a mouse. The OMAG method obtained the imagesof microstructures via SDOCT (FIG. 7A), blood flow via OMAG (FIG. 8B)and the corresponding velocities of blood flow via DOMAG (FIG. 8C).DOMAG calculated the velocities of blood flow in functional vessels,including capillaries (white arrows for example), and even in thevessels about 1.5 mm deep below the bone surface (arrow). However, thePRDOCT result (FIG. 8D) indicated that conventional DOCT failed toprovide detailed velocities of blood flow in this case. In embodiments,the level of background phase-noise may be an important metric whenquantifying blood flow, particularly in capillaries, because this metricaffects the ability to extract useful flow signals from the noisybackground. Using the method described above, the noise level for PRDOCTwas typically 0.5 rad, largely due to the heterogeneous property of thetissue sample as seen in FIG. 8D, indicating that PRDOCT may not be ableto measure blood flow velocities less than 1.1 mm/s when the A scan rateis at about 20 kHz. However, DOMAG was able to reduce this noise levelto 0.034 rad, which is comparable to the phantom experiments shownabove, indicating an approximate 15-fold improvement in imaging bloodflow velocities over conventional PRDOCT. Thus, DOMAG provides a goodtool for quantifying blood flow within a perfused tissue.

To further demonstrate the advantages of DOMAG in imaging blood flowvelocities, 3D DOMAG and PRDOCT images were compared and evaluated froma scanned tissue volume from the mouse brain cortex with the skull leftintact. FIG. 9 (shown as projection images to x-y) illustrates thedifference between OMAG, DOMAG, and PRDOCT imaging of cerebral bloodflow in mice under the same conditions. To obtain the PRDOCT flow image,algorithms were used to reduce the noise artifacts; algorithms forminimization of the sample motion artifacts, segmentation of regions ofinterest and correction of phase-wrapping errors were implemented. Itdid not require performing the segmentations in DOMAG in order to renderthe 3D image as the phase noise level was low in the entire 3D space.The physical size of scanned tissue volume was 2.5×2.5×2.0 mm³. Thewhite bar=500 μm.

These results indicate that DOMAG (FIG. 9B) reliably determined thevelocities of blood flows within almost all vessels in the scannedtissue. Not surprisingly, PRDOCT (FIG. 9C) may be erroneous inquantifying blood flows within the scanned tissue, due to noise producedin PRDOCT, which masks slow flows (<1.1 mm/s) in small vessels.Furthermore, blood vessel diameters seen in the DOMAG and OMAG imageswere significantly larger than those in the DOCT images, indicating aclear advantage of DOMAG over DOCT in quantifying blood flow in thescanned tissue.

In embodiments, the reason for these differences may be that theperformance of conventional PRDOCT is limited by the background texturenoise pattern caused by the optical heterogeneity, e.g.,microstructures, of the tissue sample. By contrast, DOMAG uses an idealreconstructed sample as the tissue background, which makes theneighboring OMAG A-scans totally correlated, maximally satisfying thecorrelation requirement for the phase-resolved technique. As aconsequence, DOMAG may reduce the background phase noise to a minimum.In embodiments, this may improve the capability of DOMAG to detect lowblood velocity near the wall of the blood vessel, and thus the diameterof blood vessels detected by DOMAG is larger than that detected byPRDOCT, as seen in FIG. 9.

In various embodiments, an alternative way to illustrate the phase noiselevels is to use 3D plots of cross-sectional images (B-scans), e.g.,FIG. 8. Shown in FIG. 10 is such an illustration for a typical B-scanfrom the cortical brain in mice. FIG. 9A shows a conventional PRDOCTflow velocity plot, without applying the segmentation approach toeliminate the random phases in low signal regions. The flow signals areindicated by the black arrows and the noise in useful signal region isindicated with a broken arrow. In the low OCT signal regions, forexample the region above the tissue surface where there is no lightreflectivity and the region deep in the tissue where the detectedoptical signal is low due to the light attenuation, the evaluated phasesmay exhibit random phase noise signals in PRDOCT.

From FIG. 10A, it is clear that the noise in the low-signal regionoverwhelmed the useful flow signals, thus segmentation is often neededin PRDOCT to exclude these random noises. After segmentation of thetissue region of interests, a better view is illustrated in FIG. 10B toshow the effects of background noise in the tissue region. In FIG. 10B,the noise indicated with the broken arrow is so high that it preventsthe small blood vessel signals from being detected. In particular, thoseindicated by arrow heads are difficult to distinguish from backgroundnoise. For DOMAG, shown in FIG. 10C, the background noise is very smallcompared to the blood vessel signals, which improves significantly theimaging performance for DOMAG.

The disclosed embodiments also include another method of performing OMAGand quantifying blood flow using DOMAG. In various embodiments, thismethod uses a phase-only filter to detect the high frequency components,which corresponds to the Doppler shift caused by the particlesmovements. FIG. 11 provides a flow chart that illustrates the steps ofthe phase-only filter method. The spectral interferogram signals foreach B-scan captured by the OMAG system are presented as g(λ,x), where λis wavelength and x is the lateral position in the B-scan. This capturedraw interferogram data g(λ,x) is first interpolated into k space g(k,x)along λ direction column by column. In embodiments, to perform thephase-only filter process, the interpolated interference fringe data maybe Fourier transformed along x direction row by row to obtain thefrequency components. For all the frequency components, the magnitudesare forced to be unity, while the phases are left unchanged so that aphase image exp(iφ(k,f)) is obtained. After this, the phase data,exp(iφ(k,f)), may then be inverse Fourier transformed along thex-direction to obtain a set of filtered interferogram signals. Inembodiments, these filtered interference signals mainly contain the highfrequency components of the raw signals, while the low frequencycomponents are suppressed. Based on these filtered signals, the OMAGmethod is further applied to abstract a flow image and obtain a flowvelocity image using DOMAG.

FIG. 12 shows an example of typical results from a single B-scan of amouse brain. FIG. 12A is the structure image, and FIG. 12B is thecorresponding flow image obtained through phase-only filter describedabove. This image localized the optical signals that are backscatteredfrom the functional blood vessels. To extract the velocity information,the DOMAG method was further applied to the filtered interferencesignal. The results are presented in FIG. 12C, which corresponds to thephase difference map obtained by DOMAG method, and can be converted tovelocity image through the following equation:

$\begin{matrix}{{v\left( {z,t} \right)} = \frac{\lambda \; \Delta \; {\phi \left( {z,t} \right)}}{4\pi \; n\; \Delta \; t}} & (10)\end{matrix}$

In various embodiments, for other phase-sensitive OCT methods, forexample optical coherence elastography, the phase difference map thatrepresents the tissue motion may also have the same problem as statedabove, e.g., the low level signals cause random phase noise. The randomnoise makes the current optical coherence elastography approach almostimpractical for use on the tissue samples both in vivo and in vitro.Using the digital background reconstruction method described above,these random phase noises can be successfully eliminated, making aquantum step for optical coherence elastography from laboratory researchto real clinical and in vivo applications.

Although certain embodiments have been illustrated and described herein,it will be appreciated by those of ordinary skill in the art that a widevariety of alternate and/or equivalent embodiments or implementationscalculated to achieve the same purposes may be substituted for theembodiments shown and described without departing from the scope. Thosewith skill in the art will readily appreciate that embodiments may beimplemented in a very wide variety of ways. This application is intendedto cover any adaptations or variations of the embodiments discussedherein. Therefore, it is manifestly intended that embodiments be limitedonly by the claims and the equivalents thereof.

What is claimed is:
 1. A method of quantifying blood perfusion in aliving tissue sample, comprising: obtaining an optical microangiography(OMAG) image of a sample, wherein the image has an OMAG backgroundsample; digitally reconstructing a homogeneous ideal static backgroundtissue; replacing the OMAG background sample with the digitallyreconstructed homogeneous ideal static background tissue; correlatingtwo or more neighboring A-lines with the digitally reconstructedhomogeneous ideal static background tissue; and measuring a phasedifference between the two or more neighboring A-lines to quantify bloodperfusion in the sample.
 2. The method of claim 1, wherein obtaining anOMAG image of the sample comprises: scanning the sample with an incidentbeam from a light source to generate two or more neighboring A-lines;detecting one or more spectral interference signals from the sample;modulating the one or more spectral interference signals while scanningthe sample in a cross-sectional direction (B scan); and obtaining atleast one image of the sample from the modulated one or more spectralinterference signals, the at least one image including a selected one ofa full range structural image of the sample, a separated structure/flowimage of the sample, and a background sample.
 3. The method of claim 1,wherein measuring the phase difference between the two or moreneighboring A-lines to quantify blood perfusion in the sample comprisesusing a phase resolved technique to extract flow velocity information.4. The method of claim 2, wherein obtaining at least one image from thesample comprises: separating structure information of the sample andflow information of the sample; and obtaining a first image and a secondimage, the first image including the structure information and thesecond image including the flow information.
 5. The method of claim 2,wherein scanning comprises scanning the sample with the incident beam inx and λ directions to obtain a first two dimensional (2-D) spectralinterferogram data set, said x direction including one or more columnsand said λ direction including one or more rows.
 6. The method of claim5, wherein obtaining the at least one image comprises: calculatingdiscrete analytic functions, along the x-direction and row by row in theλ direction of the first 2-D data set, to obtain a complex valuedfunction of the first 2-D data set; and converting the complex valuedfunction of the first 2-D data set from a spectral domain to a timedomain, column by column in the x direction, to obtain the at least oneimage of the sample.
 7. The method of claim 6, wherein calculatingdiscrete analytic functions comprises Hilbert-transforming the first 2-Ddata set.
 8. The method of claim 6, wherein converting the complexvalued function of the first 2-D data set comprises Fourier-transformingthe complex valued function of the first 2-D data set.
 9. The method ofclaim 5, further comprising scanning the sample with the incident beamin the x and λ directions along y direction to obtain a second 2-Dspectral interferogram data set, said first and second 2-D data setsforming a three-dimensional spectral interferogram data set.
 10. Themethod of claim 2, wherein said obtaining at least one image comprisesobtaining the separated structure/flow image of the sample, and whereinthe flow image of the sample is indicative of a direction of flow of thesample.
 11. The method of claim 1, further comprising filtering thespectral interference signals for each B scan using a phase-only filter.12. The method of claim 11, wherein filtering the spectral interferencesignals for each B scan using a phase-only filter comprises:Fourier-transforming interpolated fringe data along an x direction toobtain frequency components; forcing the magnitudes of the frequencycomponents to be unity while leaving the phases unchanged; andinverse-Fourier-transforming phase data along the x direction to obtaina filtered interferogram signal.
 13. A method for quantifying bloodperfusion in a sample, comprising: scanning a flowing sample with anincident beam from a light source; detecting one or more spectralinterference signals from the flowing sample to generate an OMAG bloodflow sample; digitally reconstructing a homogeneous ideal staticbackground tissue; replacing a real background sample with the digitallyreconstructed homogeneous ideal static background tissue; correlatingtwo or more neighboring A-lines with the digitally reconstructedhomogeneous ideal static background tissue; and measuring the phasedifference between the two or more neighboring A-lines to quantify bloodperfusion in the sample.
 14. The method of claim 13, wherein digitallyreconstructing a homogeneous ideal static background tissue comprisesdigitally reconstructing an ideal sample background with a constantbackscattering coefficient a₀ and a refractive index n₀ throughout thesample according to the equation:I ₀(k _(j) ,t)=2S(k _(j))E _(R)∫_(−∞) ^(∞) a ₀(z,t)cos(2k _(j) n₀(z,t)z)dz, wherein a₀(z,t)≡a₀ and n₀(z,t)≡n₀ throughout the sample. 15.The method of claim 13, wherein an OMAG blood flow signal is defined as:I′(k _(j) ,t)=2S(k _(j)){E _(R) ∫a ₀ cos(2k _(j) n ₀ z)dz+E _(R) a(z ₁,t ₁)cos [2k _(j) n(z ₁ ,t ₁)(z ₁ −vt)]}.
 16. The method of claim 15,further comprising: treating t (the time variable) as a constant; andperforming a Fourier transformation upon k (wavelength) such that{tilde over (I)}(z,t)=FT ⁻¹ {I(k _(j) ,t)}|_(k) =A(z,t)exp[iφ(z,t)]wherein ψ(z, t) is a phase of an analytic signal.
 17. The method ofclaim 16, further comprising evaluating a phase difference betweenneighboring A-scans, n and n−1, according to the equation:${\Delta \; {\phi \left( {z,t} \right)}} = {{\tan^{- 1}\left\lbrack \frac{{Im}\left\lbrack {{\overset{\sim}{I}\left( {z,t_{n}} \right)} \cdot {{\overset{\sim}{I}}^{*}\left( {z,t_{n - 1}} \right)}} \right\rbrack}{{Re}\left\lbrack {{\overset{\sim}{I}\left( {z,t_{n}} \right)} \cdot {{\overset{\sim}{I}}^{*}\left( {z,t_{n - 1}} \right)}} \right\rbrack} \right\rbrack}.}$18. The method of claim 17, further comprising generating a velocity offlow signal using the equation:${v\left( {z,t} \right)} = \frac{\lambda \; \Delta \; {\phi \left( {z,t} \right)}}{4\pi \; n\; \Delta \; t}$wherein v(z,t) is a flow velocity at depth z, wherein Δt is a timeinterval between neighboring A-lines, and wherein n is a refractiveindex of the sample.